多器官分割：标注稀缺下学习范式的渐进式探索
Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation
AI总结：
本文针对标注稀缺下的多器官分割问题，全面回顾了迁移学习、半监督学习和部分监督学习三种标注高效的学习范式，并总结了其技术方法、挑战及未来趋势。
摘要：
从医学图像中精确勾勒人体多个器官或异常区域，在计算机辅助诊断、手术模拟、图像引导干预，尤其是放疗计划中扮演着重要角色。因此，探索自动分割方法意义重大，其中基于深度学习的方法发展迅速，在多器官分割中取得了显著进展。然而，获取规模适中且细粒度的多器官标注数据集极其困难且昂贵。这种稀缺的标注限制了高性能多器官分割模型的发展，但也催生了许多标注高效的学习范式。其中，利用外部数据集的迁移学习、使用未标注数据集的半监督学习以及整合部分标注数据集的部分监督学习，已成为打破多器官分割这一困境的主导途径。我们首先回顾了传统的全监督方法，然后从技术与方法论的双重角度，对上述3种学习范式在多器官分割中的应用进行了全面系统的阐述，最后总结了它们面临的挑战与未来趋势。
英文摘要：
Precise delineation of multiple organs or abnormal regions in the human body from medical images plays an essential role in computer-aided diagnosis, surgical simulation, image-guided interventions, and especially in radiotherapy treatment planning. Thus, it is of great significance to explore automatic segmentation approaches, among which deep learning-based approaches have evolved rapidly and witnessed remarkable progress in multi-organ segmentation. However, obtaining an appropriately sized and fine-grained annotated dataset of multiple organs is extremely hard and expensive. Such scarce annotation limits the development of high-performance multi-organ segmentation models but promotes many annotation-efficient learning paradigms. Among these, studies on transfer learning leveraging external datasets, semi-supervised learning using unannotated datasets and partially-supervised learning integrating partially-labeled datasets have led the dominant way to break such dilemma in multi-organ segmentation. We first review the traditional fully supervised method, then present a comprehensive and systematic elaboration of the 3 abovementioned learning paradigms in the context of multi-organ segmentation from both technical and methodological perspectives, and finally summarize their challenges and future trends.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
